Correlation Between Bank of New York and OFS Credit
Can any of the company-specific risk be diversified away by investing in both Bank of New York and OFS Credit at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining Bank of New York and OFS Credit into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bank of New and OFS Credit, you can compare the effects of market volatilities on Bank of New York and OFS Credit and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in Bank of New York with a short position of OFS Credit. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank of New York and OFS Credit.
Diversification Opportunities for Bank of New York and OFS Credit
0.93 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Bank and OFS is 0.93. Overlapping area represents the amount of risk that can be diversified away by holding Bank of New and OFS Credit in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on OFS Credit and Bank of New York is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on Bank of New are associated (or correlated) with OFS Credit. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of OFS Credit has no effect on the direction of Bank of New York i.e., Bank of New York and OFS Credit go up and down completely randomly.
Pair Corralation between Bank of New York and OFS Credit
Allowing for the 90-day total investment horizon Bank of New is expected to generate 2.89 times more return on investment than OFS Credit. However, Bank of New York is 2.89 times more volatile than OFS Credit. It trades about 0.26 of its potential returns per unit of risk. OFS Credit is currently generating about 0.16 per unit of risk. If you would invest 7,651 in Bank of New on August 28, 2024 and sell it today you would earn a total of 471.00 from holding Bank of New or generate 6.16% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Bank of New vs. OFS Credit
Performance |
| Timeline |
| Bank of New York |
| OFS Credit |
Bank of New York and OFS Credit Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Bank of New York and OFS Credit
The main advantage of trading using opposite Bank of New York and OFS Credit positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank of New York position performs unexpectedly, OFS Credit can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in OFS Credit will offset losses from the drop in OFS Credit's long position.| Bank of New York vs. Northern Trust | Bank of New York vs. Invesco Plc | Bank of New York vs. Franklin Resources | Bank of New York vs. T Rowe Price |
| OFS Credit vs. Oxford Lane Capital | OFS Credit vs. XOMA Corporation | OFS Credit vs. HUMANA INC | OFS Credit vs. Aquagold International |
Check out your portfolio center.Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the My Watchlist Analysis module to analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like.
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